Kevin Antonio Steiner
+41 21 695 80 53
EPFL › ENAC › IIC › IMOS
Website: https://IMOS.epfl.ch
Kevin is a PhD student at École Polytechnique Fédérale de Lausanne (EPFL). He joined the Intelligent Maintenance and Operations Systems (IMOS) Lab under the supervision of Prof. Olga Fink in February 2023. Before joining IMOS, he obtained his master's degree in physics from the Karlsruhe Institute of Technology, where he conducted research in machine learning methods for accelerating chemical reaction exploration.
During the PhD he will focus on physics-informed machine learning methods and its applications to composite structures. Focusing on multiple aspects of composite damage, from detection to design optimization his goal is to improve the reliability of composite materials through improved monitoring and predictive models.
During the PhD he will focus on physics-informed machine learning methods and its applications to composite structures. Focusing on multiple aspects of composite damage, from detection to design optimization his goal is to improve the reliability of composite materials through improved monitoring and predictive models.
Education
Master of Science MSc. Physics
| Quantum Physics
2022 – 2025
KIT
Directed by
Prof. Pascal Friederich
Bachelor of Science BSc. in Physics
| Physics
2018 – 2022
KIT
Directed by
Prof. Kirill Melnikov
Selected publications
From Physics to Machine Learning and Back: Part II-Learning and Observational Bias in Prognostics and Health Management (PHM)
Olga Fink et al.
Published in Reliability Engineering & System Safety in 2026
From Physics to Machine Learning and Back: Part I-Learning with Inductive Biases in Prognostics and Health Management
Olga Fink et al.
Published in Reliability Engineering & System Safety in 2026
Generative models for crystalline materials
Houssam Metni et al.
Published in Advanced Materials in 2026
Teaching & PhD
Machine learning for predictive maintenance applications
CIVIL-426
Course Book
The course aims to develop machine learning algorithms capable of efficiently detecting faults in complex industrial and infrastructure assets, isolating their root causes, and ultimately predicting their remaining useful lifetime.
Course Book
The course aims to develop machine learning algorithms capable of efficiently detecting faults in complex industrial and infrastructure assets, isolating their root causes, and ultimately predicting their remaining useful lifetime.
Introduction to machine learning for engineers
Machine learning is a sub-field of Artificial Intelligence that allows computers to learn from data, identify patterns and make predictions. As a fundamental building block of the Computational Thinking education at EPFL, Civil students will learn ML with civil case studies (summary generated by ML)